• Proceedings of the Safety Management and Science Conference
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• 2006.04a
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• pp.45-50
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• 2006

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years $(2002\sim2004)$ in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.2
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• pp.95-103
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• 1993

In the manufacturing field, some traditional manufacturing and machining methods become weakened the productivity, the external competitive power, and accuracies of the products. In these point of view, the unmanned and intelligent manufacturing systems are proposed by some manufacturing companies. The real-time monitoring technology of the cutting tool conditions i.e. tool wear, tool breakage, crack, and chipping anre necessarily reauired to realize those system, especially. In this study, we constructed the multi- sensing system using the acceleration sensor, the current sensor, and the loadmeter of a machine tool. Also, we analyzed the nose breakage, the massive signal, and some monitoring features by means of the developed system.

• Journal of the Korea Furniture Society
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• v.28 no.3
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• pp.156-168
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• 2017

The digital information society that developed along with the 21st century is the 'Open-source Making Movement' which produces collective intelligence through open-source sharing and digital manufacturing tools, which is called 'Makers Movement'. The purpose of this paper is to analyze the case of Makers Movement in furniture design through 'IKEA Hacking' which arose simultaneously with the Makers Movement, and to study and present the prospect and direction of furniture design in the change of manufacturing industry. In this study, four design features were compared with IKEA hacking cases along with the establishment of 'community' which is a feature of Makers Movement. Four characteristics are first customized design, second derivative design through open source, third long -Tail effect design, and fourth, design using digital manufacturing tools. The prospect and direction of furniture design through this study are as follows: first, democratization of furniture design manufacturing, second job creation, third, coexistence of large and small enterprises, fourth promotion of various new technologies, and fifth, discovery of various furniture designers through 'Open System Organization'.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2235-2245
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• 2000

The necessity of using rapid prototyping(RP) for short-run manufacturing is continuously driving a development of a cost-effective technique that will produce completely-finished quality parts in a very short time. To meet these demands, the improvements in production speed, accuracy, materials, aid cost are crucial. Thus, a new hybrid-RP system performing both deposition and machining in a station is proposed. For the new hybrid RP process to maintain the same degree of process automation as in currently available processes like SLA or FDNI, a sophisticated process planning system is developed. In the process planner, CAD models(STEP AP203) are partitioned into 3D manufacturable volumes called 'Ueposition feature segment"(DFS) after machining features called "machining feature segmenf'(MFS) are extracted from the initial CAD model. Once MFS and DFS are identified, the process planner arranges them into a chain of processes and automatically generates machining information for each DFS and MFS. The goal of this paper is to present a framework for a process planning system for hybrid RP processes and to outline the geometric algorithms involved in developing such an environment.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.23-34
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• 1993

Although there are numerous studies that address the problem of optimal machine grouping and part family classification for cellular manufacturing, little research has been reported that studies the conditions where cellular manufacturing is appropriate. This paper, in order to evaluate and compare the job shop with the GT cellular shop, the performance of those shops were simulated by using SIMAN. We tested the effect of independent variables including changes of product demands, intercell flow level, group setup time, processing time variability, variety of material handling systems, and job properties (ratio of processing time and material handling time). And also performance measures (dependent variables), such as machine utilization, mean flow time, average waiting time, and throughput rate, are discussed. Job shop model and GT cellular shop written in SIMAN simulation language were used in this study. These systems have sixteen machines which are aggregated as five machine stations using the macro feature of SIMAN. The results of this research help to better understand the effect of production factors on the performance of cellular manufacturing systems and to identify some of the necessary conditions required to make these systems perform better than traditional job shops. Therefore, this research represents one more step towards the characterization of shops which may benefit from cellular manufacturing.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.441-458
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• 1996

Many researches on feature recognition have been performed up to now, but the general solution for recognizing arbitrary features has not been developed. The most popular research area in feature recognition is automatic extraction of 2.5 dimensional features, because they are frequently used in manufacturing field. In this paper, a faster and more convenient 2.5 dimensional feature recognition algorithm is proposed using a new strategy which is quite different from the existing ones. The proposed algorithm takes process planning into consideration. The algorithm is implemented in C++. By applying the algorithm to practical complicate examples, we verify that the algorithm is working very well.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.111-122
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• 2010

In this study, a length adjustment algorithm for cyclic signals in manufacturing process using Time Invariant Feature point Extraction and Matching(TIFEM) is proposed. In order to precisely compensate the length of cyclic signals which have irregular length in the middle of signal as well as in the full length more feature points are needed. The extracted feature must involve information about the pattern of signal and should have invariant properties on time and scale. The proposed TIFEM algorithm extracts features having the intrinsic properties of the signal characteristics at first. By using those extracted features, feature vector is constructed for each time point. Among those extracted features, the only effective features are filtered and are chosen such as basis for the length adjustment. And then the partial length adjustment is performed by matching feature points. To verify the performance of the proposed algorithm, the experiments were performed with the experimental data mimicking the three kinds of signals generated from the actual semiconductor process.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.415-424
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• 2002

Semiconductor manufacturing has been emerged as a highly competitive but profitable business. Accordingly it becomes very important for semiconductor manufacturing companies to meet customer demands at the right time, in order to keep the leading edge in the world market. However, due-date oriented production is very difficult task because of the complex job flows with highly resource conflicts in fabrication shop called FAB. Due to its cyclic manufacturing feature of products, to be completed, a semiconductor product is processed repeatedly as many times as the number of the product manufacturing cycles in FAB, and FAB processes of individual manufacturing cycles are composed with similar but not identical unit processes. In this paper, we propose a production scheduling and control scheme that is designed specifically for semiconductor scheduling environment (FAB). The proposed scheme consists of three modules: simulation module, cycle due-date estimation module, and dispatching module. The fundamental idea of the scheduler is to introduce the due-date for each cycle of job, with which the complex job flows in FAB can be controlled through a simple scheduling rule such as the minimum slack rule, such that the customer due-dates are maximally satisfied. Through detailed simulation, the performance of a cycle due-date based scheduler has been verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.78-81
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• 2021

Through the smart factory construction project, sensors can be installed in manufacturing production facilities and various process data can be collected in real time. Through this, research on real-time facility anomaly detection is being actively conducted to reduce production interruption due to facility abnormality in the manufacturing process. In this paper, to detect abnormalities in production facilities, the manufacturing data was applied to deep learning models Autoencoder(AE), VAE(Variational Autoencoder), and AAE(Adversarial Autoencoder) to derive the results. Manufacturing data was used as input data through a simple moving average technique and preprocessing process, and performance analysis was conducted according to the window size of the simple movement average technique and the feature vector size of the AE model.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.335-340
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• 2014

Crowd Sourcing-based Social App Manufacturing environment, a small app development project by a team of anonymous virtual performed without the constraints of time and space, and manage it for the app development process need to be automated method. Virtual teams with anonymity is a feature of the Social App Manufacturing, is an important factor that increases the uncertainty of whether the completion of the project or reduction in visibility of the progress of the project. In this study, as one of how to manage the project of Social App Manufacturing environment, the impact of risk that can be used to quantitatively measure the impact of the risk of delay in development has on the project also proposes a measurement model. Effects of risk and type of the impact of risks associated with delays in the work schedule also define the characteristic function, measurement model that has been proposed, suggest the degree of influence measurement equation of risk of the project in accordance with the progressive. The advantage of this model, the project manager is able to ensure the visibility of the progress of the project. In addition, identify the project risk of work delays, and to take precautions.